Revolutionizing Organizational Decision-Making for Stock Market: A Machine Learning Approach with CNNs in Business Intelligence and Management

Malay sarkar, Rasel Mahmud Jewel, Md Salim Chowdhury, Md Al-Imran, Rumana Shahid Sawalmeh, Aishwarya Roy puja, Rejon Kumar Ray, Sandip Kumar Ghosh
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Abstract

This research delves into the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management and business intelligence. The study follows a comprehensive methodology, emphasizing the importance of high-quality datasets in leveraging deep learning for enhanced decision-making. Results demonstrate the superior performance of CNN models over traditional algorithms, with CNN (VGG16) achieving an accuracy rate of 89.45%. The findings underscore the potential of deep learning in extracting meaningful insights from complex data, offering a paradigm shift in optimizing various organizational processes. The article concludes by emphasizing the significance of investing in infrastructure and expertise for successful CNN integration, ensuring ethical considerations, and addressing data privacy concerns. This research contributes to the growing discourse on the application of deep learning in organizational management, providing a valuable resource for businesses navigating the dynamic landscape of the global market.
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彻底改变股票市场的组织决策:商业智能与管理中的 CNN 机器学习方法
本研究深入探讨了深度学习,特别是卷积神经网络(CNN)(如 VGG16、ResNet50 和 InceptionV3)对组织管理和商业智能的变革性影响。该研究采用了一种全面的方法,强调了高质量数据集在利用深度学习增强决策方面的重要性。结果表明,CNN 模型的性能优于传统算法,其中 CNN (VGG16) 的准确率达到 89.45%。研究结果强调了深度学习在从复杂数据中提取有意义见解方面的潜力,为优化各种组织流程提供了范式转变。文章最后强调了投资基础设施和专业知识对于成功整合 CNN、确保道德考量和解决数据隐私问题的重要意义。这项研究为深度学习在组织管理中的应用这一日益增长的讨论做出了贡献,为企业驾驭全球市场的动态格局提供了宝贵的资源。
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